Medical decision making support apparatus and control method for the same

ABSTRACT

A medical decision making support apparatus performs the inference processing of obtaining an inference result by performing inference processing associated with medical diagnosis based on a plurality of pieces of input medical information, and the calculation processing of calculating the degree of denial or affirmation of the inference result in association with each of a plurality of partial sets including each medical information extracted from the plurality of pieces of medical information as an element. The medical decision making support apparatus presents a user an inference result obtained by the inference processing and negative information indicating medical information included in a partial set, of the plurality of partial sets, for which the degree of denial is calculated by the calculation processing.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of International Patent ApplicationNo. PCT/JP2010/000601, filed Feb. 2, 2010, which claims priority fromJapanese Patent Application No. 2009-047021, filed Feb. 27, 2009. Thedisclosures of both applications are hereby incorporated by referenceherein in their entirety.

TECHNICAL FIELD

The present invention relates to a medical decision making supportapparatus that processes medical information and presents the obtainedinformation, and a control method for the same.

BACKGROUND ART

In the medical field, doctors display medical images obtained by imagingpatients on monitors, interpret the displayed medical images, andobserve the states of lesions and their changes over time. Medical imagedata of this type are generated by, for example,

CR (Computed Radiography) apparatus,

CT (Computed Tomography) apparatus,

MRI (Magnetic Resonance Imaging) apparatus,

PET (Positron Emission Tomography) apparatus,

SPECT image (Single Photon Emission Computed Tomography), and

ultrasound apparatus (US: Ultrasound System).

With the aim of reducing the load of such interpretation on a doctor, anapparatus has been developed that detects, for example, an abnormaltumor shadow indicating a cancer or the like or a high-density minutecalcification shadow from a medical image, and infers and presents thestate of the shadow by computer processing, thereby supportingdiagnosis. Such a support can reduce the load of interpretation on adoctor and improve the accuracy of an interpretation result. Such anapparatus is called a computer-aided diagnosis (CAD) apparatus.

In general, the following is a proper procedure when using such a CAD inan actual clinical case. First of all, the doctor interprets medicalimages first, and then refers to the diagnosis support informationoutput from the CAD to compare it with the interpretation resultobtained by himself/herself. In this operation, more specifically, thedoctor associates finding information on an interpretation report, whichthe doctor has written by himself/herself, with finding information ofthe diagnosis support information calculated by the CAD to find anoversight, a detection error, a difference in finding, and the like. If,however, the CAD presents no grounds on which to infer the diagnosissupport information, the doctor cannot determine whether the inferenceresult obtained by the CAD is reliable or not. When the interpretationresult obtained by the doctor differs from the result obtained by theCAD, in particular, it is important to determine the reliability of theinference result.

It is therefore necessary to provide a mechanism for presenting groundson which the CAD system infers diagnosis support information. Withregard to this, patent reference 1 discloses a technique ofsuperimposing and displaying a marker indicating an abnormal shadowcandidate and information supporting the determination of abnormality ona medical image. In addition, patent reference 2 discloses a techniqueof displaying features and criteria used for computer-aided detection ascoded descriptors on an image. According to patent references 1 and 2described above, it is possible to more accurately decide the types ofabnormal shadow candidates by presenting the user the grounds ofinference with respect to detected abnormal shadows.

PRIOR ART REFERENCE Patent Reference

Patent Reference 1: WO2005/104953 Patent Reference 2: PCT(WO)2006-500124

NON-PATENT REFERENCE

Non-patent Reference 1: FinnV. Jensen, Thomas D. Nielsen, “BayesianNetworks and Decision Graphs”, 2007 (non-patent reference 1 is referredto in “DESCRIPTION OF EMBODIMENTS”)

Although the technique described in patent reference 1 presents a reasonfor the detection of an abnormal shadow candidate, it presents only onereason and provides no way of handling a case in which there are manypieces of information as the grounds of inference. The techniquedescribed in patent reference 2 allows the selection of a plurality ofpieces of reason information to be displayed. However, the user is incharge of selecting information to be presented, and the technique makesno determination about which information is selected and presented tothe user. In addition, the techniques disclosed in patent references 1and 2 present only affirmative information for an inference result, andhence the user can only determine the reliability of the inferenceresult from the affirmative information alone.

SUMMARY OF THE INVENTION

According to an embodiment of the present invention, there is provided amedical decision making support apparatus which allows a user to easilyand reliably determine the reliability of the inference result obtainedby medical decision making support.

In order to achieve the above object, a medical decision making supportapparatus according to an aspect of the present invention has thefollowing arrangement. That is,

this apparatus comprises

inference means for obtaining an inference result by performinginference processing associated with a medical diagnosis based on aplurality of pieces of input medical information,

calculation means for calculating a degree of one of denial andaffirmation of the inference result in association with each of aplurality of partial sets including each medical information extractedfrom the plurality of pieces of medical information as an element, and

presentation means for presenting an inference result obtained by theinference means and negative information indicating medical informationincluded in a partial set, of the plurality of partial sets, for which adegree of denial is calculated by the calculation means.

According to an aspect of the present invention, a user can easily andreliably determine the reliability of the inference result obtained bymedical decision making support.

Other features and advantages of the present invention will be apparentfrom the following description taken in conjunction with theaccompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate embodiments of the invention and,together with the description, serve to explain the principles of theinvention.

FIG. 1 is a block diagram showing an example of the configuration of amedical decision making support apparatus according to an embodiment;

FIG. 2 is a flowchart showing a processing procedure in the first tothird embodiments;

FIG. 3 is a flowchart showing a detailed processing procedure in stepS204 in FIG. 2;

FIG. 4A is a view showing a probability reasoning model using a Bayesiannetwork;

FIG. 4B is a view showing a probability reasoning model using a Bayesiannetwork;

FIG. 5 is a view showing an example in which several evidences are inputto the probability reasoning model in FIG. 4B;

FIG. 6 is a view showing an example in which one evidence is input tothe probability reasoning model in FIG. 4B;

FIG. 7 is a view showing an example of display on a monitor 104 when k=1in the first embodiment;

FIG. 8 is a view showing an example in which two evidences are input tothe probability reasoning model in FIG. 4B;

FIG. 9 is a view showing an example of display on the monitor 104 whenk=2 in the first embodiment;

FIG. 10 is a flowchart showing a detailed processing procedure in stepS204 in FIG. 2;

FIG. 11 is a flowchart showing a detailed processing procedure in stepS205 in FIG. 2;

FIG. 12 is a view showing an example of display on a monitor 104 whenk=1 in the second embodiment;

FIG. 13 is a view showing an example of display on the monitor 104 whenk=2 in the second embodiment;

FIG. 14 is a flowchart showing a detailed processing procedure in stepS205 in FIG. 2; and

FIG. 15 is a view showing an example of display on a monitor 104 in thethird embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

Preferred embodiments of a medical decision making support apparatus andmethod according to the present invention will be described in detailbelow with reference to the accompanying drawings. The scope of thepresent invention is not limited to the embodiments shown in theaccompanying drawings.

First Embodiment

FIG. 1 is a block diagram showing an example of the configuration of amedical decision making support apparatus according to the firstembodiment. A medical decision making support apparatus 1 obtains aninference result by performing inference processing associated withmedical diagnosis based on a plurality of pieces of input medicalinformation, and includes a control unit 10, a monitor 104, a mouse 105,and a keyboard 106. The control unit 10 includes a central processingunit (CPU) 100, a main memory 101, a magnetic disk 102, and a displaymemory 103. The CPU 100 executes programs stored in the main memory 101to perform various types of control such as communication between amedical image database 2 and a medical record database 3 and overallcontrol of the medical decision making support apparatus 1.

The CPU 100 mainly controls the operation of each constituent element ofthe medical decision making support apparatus 1. The main memory 101stores a control program to be executed by the CPU 100 and provides awork area when the CPU 100 executes a program. The magnetic disk 102stores an operating system (OS), device drives for peripheral devices,various kinds of application software including programs for executing,for example, diagnosis support processing (to be described later), andthe like. The display memory 103 temporarily stores display data for themonitor 104. The monitor 104 is, for example, a CRT monitor or a liquidcrystal monitor, and displays images based on data from the displaymemory 103. Although an inference result is displayed on the monitor 104designed to present a user (doctor) the inference results and the likeobtained by medical decision making support in this embodiment, theembodiment may take a form of outputting inference results using aprinter or the like. The mouse 105 and the keyboard 106 are operated bythe user to perform pointing input operation and input characters andthe like. The respective constituent elements described above areconnected to each other via a common bus 107.

In this embodiment, the medical decision making support apparatus 1reads out image data from the medical image database 2 and medicalrecord data from the medical record database 3 via a LAN 4. In thiscase, it is possible to use an existing PACS (Picture Archiving andCommunication System) as the medical image database 2. It is alsopossible to use, as the medical record database 3, an electronic medicalrecord system which is a subsystem of an existing HIS (HospitalInformation System). Alternatively, an external storage device such asan FDD, HDD, CD drive, DVD drive, MO drive, and ZIP drive may beconnected to the medical decision making support apparatus 1 to allow itto read out image data and medical record data from the drives.

Note that medical images include a simple X-ray image (radiogram), X-rayCT image, MRI image, PET image, SPECT image, and ultrasonic image. Inaddition, medical record data to be written include the personalinformation of each patient (name, birth date, age, sex, and the like),clinical information (various examination values, chief complaint, pasthistory, treatment history, and the like), reference information for theimage data of each patient stored in the medical image database 2, andfinding information obtained from a doctor in charge. Furthermore, aconfirmed diagnosis name is written on medical record data at aprogressed stage of diagnosis.

A way in which the control unit 10 controls the medical decision makingsupport apparatus 1 will be described next with reference to theflowchart of FIG. 2. Note that the CPU 100 implements the processingshown in the flowchart of FIG. 2 by executing a program stored in themain memory 101.

In step S201, the CPU 100 inputs desired medical image data to themedical decision making support apparatus 1 in accordance with inputsvia the mouse 105 and the keyboard 106. The medical image data input instep S201 will be referred to as an interpretation target imagehereinafter. In this image data input processing, for example, the CPU100 receives medical image data as an interpretation target image fromthe medical image database 2, which stores captured medical image data,via the LAN 4. Alternatively, the CPU 100 reads out image data as aninterpretation target image from each type of storage medium such as anFDD, CD-RW drive, MO drive, or ZIP drive connected to the medicaldecision making support apparatus 1. In step S202, the CPU 100 displaysthe interpretation target image input to the medical decision makingsupport apparatus 1 on the monitor 104.

In step S203, the doctor inputs interpretation findings to the medicaldecision making support apparatus 1 by using the mouse 105 and thekeyboard 106 while seeing the interpretation target image displayed onthe monitor 104. At this time, it is possible to use an interpretationfinding input support method using a template form. Alternatively, it ispossible to input the image feature amounts obtained by imageprocessing. Each input interpretation finding/image feature amount willbe referred to as medical information hereinafter. In step S204, the CPU100 executes the processing of obtaining medical diagnosis informationfrom the medical information of the interpretation target image input instep S203 by computer processing. That is, the CPU 100 performsinference processing for the medical information input to the medicaldecision making support apparatus 1. A detailed processing procedure instep S204 will be described below with reference to FIG. 3. Dataindicated by I, for example, I_(fix), represents a set of one or morepieces of medical information.

FIG. 3 is a flowchart showing a detailed processing procedure in stepS204. In step S301, the CPU 100 acquires the probabilities (prioriprobabilities) of inference results A₁ to A_(n) by using a probabilityreasoning model when no evidence (to be described later) is input, andstores the resultant information in the main memory 101. Thisprobability reasoning model is, for example, a Bayesian network likethat shown in FIGS. 4A and 4B.

A Bayesian network is a model expressing phenomena with a plurality ofevents and the causality relationships between the events. Therelationship between events is represented by a probability. Eventsconstituting a target phenomenon are represented by a node 401. Therelationship between nodes is represented by a link 402. Each link isexpressed by an arrow. A node at the root of each arrow will be referredto as a parent node. A node at the point of each arrow will be referredto as a child node. Each node has a plurality of states 403 indicatingthe states of the node. An occurrence probability (priori probability)is assigned to each state. The relationship between a parent node and achild node is given by a conditional probability conditioned on theparent node. A table of such conditional probabilities will be referredto as a conditional probability table 404 (CPT: Conditional ProbabilityTable).

Information indicating in which state at least one node of a targetmodel is will be referred to as an evidence. It is possible to obtainthe probability (called posteriori probability) of a target node by abelief propagation method using this evidence, CPT, and Bayes' theorem(equation (1) (non-patent reference 1)).

$\begin{matrix}{\left\lbrack {{Mathematical}\mspace{14mu} 1} \right\rbrack \mspace{571mu}} & \; \\{{P\left( {AB} \right)} = \frac{{P\left( {BA} \right)}{P(A)}}{P(B)}} & (1)\end{matrix}$

FIGS. 4A and 4B show a reasoning model associated with abnormal shadowsin the lungs, with each node corresponding to a finding from aninterpretation doctor. For example, “calcification density ratio”represents the ratio of a calcified portion in an abnormal shadow to theshadow. Likewise, “water density ratio”, “soft tissue density ratio”,and “gas density ratio” each are its ratio to the shadow. “Vascularinvolvement/involution” represents the occurrence/non-occurrence ofvascular involvement/involution inside organs in the surrounding lungfields. Note that densities associated with abnormal shadows includethose of substances other than those described above (for example, ametal). In some cases, such information is not input as medicalinformation. That is, the total sum is not always 100%.

FIG. 4B shows a state in which no evidence is input. The numerical valueprovided on a side of each state of each node indicates the prioriprobability of the state. For example, the priori probabilities of therespective states of “abnormality type” are follows: “abnormality type:primary lung cancer”: 11.0%, “abnormality type: lung metastasis”: 48.0%,and “abnormality type: other abnormalities”: 41.0%. FIG. 5 shows a statein which evidences are input to some of the plurality of nodes in theBayesian network shown in FIG. 4B. Each input evidence corresponds to aprobability of 100% as the probability of a corresponding state as of“boundary”, “shape”, or the like.

In step S302, the CPU 100 sets the m (m≧1) pieces of medical informationinput in step S203 as fixed information (to be referred to as I_(fix)hereinafter), and calculates probabilities belonging to inferenceresults A₁ to A_(n) (n≧2) by a probability reasoning model using I_(fix)as evidences. That is, the fixed information I_(fix) is a set of mpieces of medical information. In step S303, the CPU 100 selects aninference result A_(x) (1≦x≦n) with the highest probability of theposteriori probabilities calculated in step S302. At the same time, themain memory 101 stores the inference results A₁ to A_(n) and theirprobabilities. In addition, a variable j is prepared, and j=1 is set.

In step S304, the CPU 100 selects k (1≦k≦m) pieces of medicalinformation of I_(fix) and stores them as partial information I_(j) ofthe fixed information in the main memory 101. That is, the partialinformation I_(j) is a partial set of the fixed information I_(fix)which is obtained by extracting k pieces of medical information from thefixed information I_(fix). In step S305, the CPU 100 calculates aposteriori probability belonging to the inference result A_(x) by theprobability reasoning model using I_(j) selected in step S304 asevidences. The CPU 100 calculates the difference (to be referred to asD(A_(x)|I_(j))) between the calculation result and the prioriprobability of A_(x) acquired in step S301, and stores the difference inassociation with I_(j) stored in the main memory 101 in step S304. TheCPU 100 then adds 1 to the variable j. In this manner, the CPU 100calculates the degree of denial or affirmation with respect to theinference result obtained in step S303 for each of the plurality ofpartial sets I_(j) including the pieces of medical information extractedas elements from the plurality of pieces of medical information.

In step S306, the CPU 100 compares the value of j with the total numberof combinations (to be referred to as a combination count hereinafter)of I_(j) k pieces of medical information selected from I_(fix). If j issmaller than the combination count, since the CPU 100 has not acquiredthe posteriori probabilities of all I_(j), the process returns to stepS304 to continue the above processing. If j is larger than thecombination count, the CPU 100 executes step S307.

In step S307, the CPU 100 compares D(A_(x)|I_(j)) associated with I_(j)stored in step S304. The CPU 100 stores, in the main memory 101, I_(j)(to be referred to as I_(H) hereinafter) from which maximumD(A_(x)|I_(j)) (with the maximum absolute value) is obtained amongpositive D(A_(x)|I_(j)). The CPU 100 also stores, in the main memory101, I_(j) (to be referred to as I_(L) hereinafter) from which minimumD(A_(x)|I_(j)) (with the maximum absolute value) is obtained amongnegative D(A_(x)|I_(j)). At this time, if there is no I_(j) whoseD(A_(x)|I_(j)) is positive, a NULL value is input to I_(H). Likewise, ifthere is no I_(j) whose D(A_(x)|I_(j)) is negative, a NULL value isinput to I_(L). If a NULL value is input, I_(H) or/and I₁ is notdisplayed in step S205. I_(j) whose D(A_(x)|I_(j)) is positive indicatesinformation which increases the probability of the inference resultA_(x). I_(j) whose D(A_(x)|I_(j)) is negative indicates informationwhich decreases the probability of the inference result A_(x).Therefore, I_(H) is a reason for affirming the inference result, andI_(L) is a reason for denying the inference result.

In step S205, the CPU 100 displays the inference processing resultsprocessed in step S204. The CPU 100 displays the inference results A₁ toA_(n), their posteriori probabilities, and I_(H) and I_(L) stored in themain memory 101 on the monitor 104.

Cases in which k=1 and k=2 will be described below as concrete examples.

In step S301, the CPU 100 acquires 11.0%, 48.0%, and 41.0% which arerespectively the priori probabilities of the inference results:“abnormality type: primary lung cancer”, “abnormality type: lungmetastasis”, and “abnormality type: other abnormalities”. In step S302,the CPU 100 calculates the posteriori probabilities of the inferenceresults: “abnormality type: primary lung cancer”, “abnormality type:lung metastasis”, and “abnormality type: other abnormalities” by usingI_(fix) input in step S203 as evidences. The calculated posterioriprobabilities of the respective inference results are 15.3%, 66.4%, and18.3%, respectively (FIG. 5). The CPU 100 therefore stores thesecalculation results and selects “abnormality type: lung metastasis” withthe highest posteriori probability in step S303.

FIG. 6 shows a case in which the medical information “size of nodus:intermediate” is selected from I_(fix) by setting k=1, and is set aspartial information I₁ of the fixed information. The CPU 100 calculatesa posteriori probability belonging to the inference result “abnormalitytype: lung metastasis” selected in step S303 by using I₁ as an evidence.The CPU 100 then calculates the difference between 53.5% obtained as acalculation result and the posteriori probability, 48.0%, of“abnormality type: lung metastasis” obtained in step S301. The CPU 100stores 5.5% obtained as a result in association with I₁.

Table 1 shows all I_(j) with k=1, the posteriori probabilities of theinference result “abnormality type: lung metastasis” calculated by usingI_(j) as evidences, and the differences between the posterioriprobabilities and the priori probabilities. Comparing the differenceswill reveal that when I_(j) is “shape: sphere”, I_(j) takes the maximumdifference, 17.9%, among the positive differences, whereas when I_(j) is“gas density ratio: high”, I_(j) takes the maximum difference, −11.0%,among the negative differences. Therefore, I_(H) is “shape: sphere”, andI_(L) is “gas density ratio: high”.

TABLE 1 I_(j)(k = 1) P(A_(x)|I_(j)) D(A_(x)|I_(j)) Size of Nodus:Intermediate 53.5% 5.5% Shape: Sphere 65.9% 17.9% Soft Tissue DensityRatio: Low 40.4% −7.6% Gas Density Ratio: High 37.0% −11.0% Boundary:Unclear 59.9% 11.9% P(A_(x) = A₂) = 48.0%

FIG. 7 shows an example of display on the monitor 104 when k=1. FIG. 7shows “abnormality type: primary lung cancer”, “abnormality type: lungmetastasis”, and “abnormality type: other abnormalities” as theinference results A₁ to A_(n) and, and 15.3%, 66.4%, and 18.3%respectively calculated as the posteriori probabilities of the inferenceresults by using I_(fix) as evidences. FIG. 7 further shows I_(H)“shape: sphere” as a reason for affirming the inference result with thehighest probability, and I_(L) “gas density ratio: high” as a reason fordenying the inference result.

FIG. 8 shows a case in which the pieces of medical information “size ofnodus: intermediate” and “shape: sphere” are selected from I_(fix) bysetting k=2, and are set as partial information I₁ of fixed information.The CPU 100 calculates a posteriori probability belonging to theinference result “abnormality type: lung metastasis” selected in stepS303 by using I₁ as evidences. The CPU 100 then calculates thedifference between 71.1% obtained as a calculation result and the prioriprobability, 48.0%, of “abnormality type: lung metastasis” obtained instep S301. The CPU 100 stores 23.1% obtained as a result in associationwith I₁.

Table 2 shows all I_(j) with k=2, the posteriori probabilities of theinference result “abnormality type: lung metastasis” calculated by usingI_(j) as evidences, and the differences between the posterioriprobabilities and the priori probabilities. Comparing the differenceswill reveal that when I_(j) is “shape: sphere” and “boundary: unclear”,I_(j) takes the maximum difference, 27.2%, among the positivedifferences, whereas when I_(j) is “soft tissue density ratio: low” and“gas density ratio: high”, I_(j) takes the minimum difference, −21.7%,among the negative differences. Therefore, I_(H) is “shape: sphere” and“boundary: unclear”, and I_(L) is “soft tissue density ratio: low” and“gas density ratio: high”.

TABLE 2 I_(j)(k = 2) P(A_(x)|I_(j)) D(A_(x)|I_(j)) Size of Nodus: Shape:Sphere 71.1% 23.1% Intermediate Size of Nodus: Soft Tissue Density 44.6%−3.4% Intermediate Ratio: Low Size of Nodus: Gas Density Ratio: 40.6%−7.4% Intermediate High Size of Nodus: Boundary: Unclear 65.4% 17.4%Intermediate Shape: Sphere Soft Tissue Density 60.5% 12.5% Ratio: LowShape: Sphere Gas Density Ratio: 57.9% 9.9% High Shape: Sphere Boundary:Unclear 75.2% 27.2% Soft Tissue Gas Density Ratio: 26.3% −21.7% DensityRatio: High Low Soft Tissue Boundary: Unclear 53.8% 5.8% Density Ratio:Low Gas Density Boundary: Unclear 50.8% 2.8% Ratio: High P(A_(x) = A₂) =48.0%

FIG. 9 shows an example of display on the monitor 104 when k=2. FIG. 9shows “abnormality type: primary lung cancer”, “abnormality type: lungmetastasis”, and “abnormality type: other abnormalities” as theinference results A₁ to A_(n), and 15.3%, 66.4%, and 18.3% respectivelycalculated as the posteriori probabilities of the inference results byusing I_(fix) as evidences. FIG. 9 further shows I_(H) “shape: sphere”and “boundary: unclear” as reasons for affirming the inference resultwith the highest posteriori probability, and I_(L) “soft tissue densityratio: low” and “gas density ratio: high” as reasons for denying theinference result.

In either of the cases of k=1 and k=2, it is preferable to display theinference results A₁ to A_(n) in descending order of posterioriprobability calculated by using I_(fix) as evidences. However, thepresent invention is not limited to this.

Displaying both reasons for affirming an inference result and reasonsfor denying the inference result can make the user feel, for example,the necessity to determine the reliability of the presented inferenceresult, verify the reliability of input medical information, andconsider a diagnosis other than the presented inference result.

According to the arrangement described above, the following effects canbe obtained:

(1) Performing inference using part of medical information afterinference using the medical information of an abnormal shadow makes itpossible to determine medical information contributing to an inferenceresult with the highest probability and narrow down and presentinformation as grounds for inference.(2) Presenting inference reasons for denying an inference result canmake the user feel, for example, the necessity to verify the reliabilityof input medical information and consider a diagnosis other than thepresented inference result with the highest probability.

Modification of First Embodiment

Step S201 is not limited to the input of medical image data. It ispossible to input medical examination data including an interpretationreport and information necessary for diagnosis support processing. Inthis case, the apparatus may have an arrangement for allowing the userto directly input these data or an arrangement capable of reading datafrom various types of storage media such as an FDD, CD-RW drive, MOdrive, and ZIP drive on which information is recorded. It is alsopossible to provide an arrangement for allowing the medical decisionmaking support apparatus 1 to receive these data from a database, onwhich the data are recorded, by connecting the medical decision makingsupport apparatus 1 to the database via a LAN.

In addition, the generation of diagnosis information by inferenceprocessing in step S204 may take the following form. That is, aprocessing target is not limited to medical image data. For example, aprocessing target can include medical examination data including a pastinterpretation report or medical record concerning an object or othertypes of information which can be used for diagnosis support processing.In this case, it is possible to generate diagnosis information based onmedical examination data other than image information of an object.

In addition, when partial information I_(j) of fixed information is tobe selected in step S304, k pieces of information of I_(fix) or less maybe selected. For example, in each of the cases of k=1 and k=2, thepartial information I_(j) may be acquired, and I_(H) and I_(L) describedabove may be acquired by using these pieces of information.

Furthermore, it is possible to select a plurality of reasons foraffirming an inference result or reasons for denying the inferenceresult in step S307. In this case, the user may determine the number ofreasons to be selected, or all reasons exceeding a given threshold maybe selected. In this case, the user may determine a threshold. If thereis a reason for denial, a warning may be displayed. An example ofdetermining the execution/non-execution of display by using a thresholdwill be described in the second embodiment.

In step S205, all the inference results A₁ to A_(n) are displayed.However, only an inference result with the highest posterioriprobability may be displayed. Alternatively, only some inference resultsmay be displayed. In this case, the user may determine the number ofinference results to be selected. In addition, inference results withposteriori probabilities exceeding a threshold, for example, posterioriprobabilities equal to or more than 30%, may be displayed. However, thethreshold to be set is not limited to the above example, and the usermay determine a threshold.

Second Embodiment

The second embodiment will be described next. Note that the arrangementof the second embodiment is the same as that of the first embodiment.For this reason, the block diagram of FIG. 1 will be used, and adescription of the arrangement will be omitted. Control performed by acontrol unit 10 in the second embodiment is almost the same as that inthe first embodiment (FIG. 2). However, this control differs from thatin the first embodiment in the inference processing in step S204 and theinference result display processing in step S205. These processes willbe described below with reference to the flowcharts of FIGS. 10 and 11.

FIG. 10 is a flowchart showing a detailed processing procedure in stepS204 in the second embodiment. In step S601, a CPU 100 acquiresprobabilities (priori probabilities) of inference results A₁ to A_(n)without any evidence input by using a probability reasoning model, andstores them in a main memory 101. In step S602, the CPU 100 calculatesprobabilities belonging to the predetermined inference results A₁ toA_(n) (n≧2) by using the probability reasoning model using m (m≧1)pieces of medical information input in step S503 as fixed information(to be referred to as I_(fix) hereinafter). The CPU 100 also prepares avariable j and sets j=1.

In step S603, the CPU 100 selects k (1≦k≦m) pieces of medicalinformation of I_(fix), and stores them as partial information I_(j) ofthe fixed information in the main memory 101. In step S604, the CPU 100calculates posteriori probabilities belonging to the inference resultsA₁ to A_(n) by the probability reasoning model using the tentativeinformation I_(j) selected in step S603 as evidences. The CPU 100 storesthe calculation results in the main memory 101 in associated with storedI_(j) in step S603. In step S605, the CPU 100 comparatively calculatesthe priori probabilities of the inference results A₁ to A_(n) obtainedin step S601 and the posteriori probabilities of the inference resultsA₁ to A_(n) by using I_(j) obtained in step S604 as evidences, andstores the calculation results in association with I_(j). The CPU 100then adds 1 to the variable j. As a comparative calculation method, forexample, a method of calculating the differences between posterioriprobabilities and priori probabilities is available.

In step S606, the CPU 100 compares the value of j with the total numberof combinations (to be referred to as a combination count hereinafter)of I_(j) k pieces of medical information selected from I_(fix). If j issmaller than the combination count, since the CPU 100 has not acquiredthe posteriori probabilities of all I_(j), the process returns to stepS603 to continue the above processing. If j is larger than thecombination count, the CPU 100 executes step S607. In step S607, the CPU100 calculates values (to be referred to as relationship amountsC(A_(i), I_(j)) hereinafter) indicating the relationships between thecalculation results associated with I_(j) obtained in step S605 and theinference results, and stores the calculation results in associationwith I_(j). For example, relationship amounts are calculated by

a method of determining a relationship amount from the differencebetween a calculated posteriori probability and a priori probability andtable 4 described later for each partial set, or

a method of calculating the absolute values of the differences betweencalculated posteriori probabilities and priori probabilities for therespective partial sets, and normalizing the values with reference tothe maximum value among them.

This relationship amount calculation corresponds to the calculation of adegree associated with an inference result.

With the above operation, the processing in step S204 is terminated. Instep S205, the CPU 100 displays the inference processing resultsprocessed in step S204. A detailed processing procedure in step S205will be described below with reference to FIG. 11.

FIG. 11 is a flowchart showing the detailed processing procedure (forinference result display) in step S205. In step S701, the CPU 100prepares a variable i in the main memory 101 and sets i=1. In step S702,the CPU 100 prepares a variable j in the main memory 101 and sets j=1.In step S703, the CPU 100 determines whether a relationship amountstored in the main memory 101 satisfies a predetermined criterion. Ifthe relationship amount satisfies the predetermined criterion, the CPU100 performs the processing in step S704. The process then advances tostep S705. If the relationship amount does not satisfy the criterion,the CPU 100 skips the processing in step S704. In step S704, the CPU 100displays I_(j) stored in the main memory 101 on a monitor 104. At thistime, the CPU 100 simultaneously displays whether I_(j) is a reason foraffirming the inference result A_(i) or a reason for denying it, inaccordance with the relationship amount. A reason for affirming theinference result A_(i) is information that increases the probability ofthe inference result A_(i). A reason for denying the inference result isinformation that decreases the probability. If, for example,relationship amounts are defined as indicated by table 4 (to bedescribed later), a relationship amount having a positive value is areason (affirmative information) for affirming the inference result. Arelationship amount having a negative value is a reason (negativeinformation) for denying the inference result.

In step S705, the CPU 100 adds 1 to the variable j stored in the mainmemory 101.

In step S706, the CPU 100 compares the value of j with the combinationcount (mCk). If j is smaller than the combination count, since it isimpossible to determine whether the relationship amounts associated withall I_(j) satisfy the determination criterion, the process returns tostep S703 to continue the processing. If j is larger than thecombination count, the CPU 100 executes step S707.

In step S707, the CPU 100 displays, on the monitor 104, the posterioriprobability of the inference A_(i) obtained when I_(fix) stored in themain memory 101 is input as evidences. This corresponds to the inferenceresult desired by the user. In step S708, the CPU 100 adds 1 to thevalue of the variable i stored in the main memory 101. In step S709, theCPU 100 compares the value of i with the value of n. If i is smallerthan n, since not all the inference results A_(i) have been processed,the process returns to step S702 to continue the processing. If i islarger than n, the CPU 100 terminates the processing in step S205. Withthis processing, posteriori probabilities, affirmative information, andnegative information are displayed for all the inference results A_(i).

Cases in which k=1 and k=2 will be described below as concrete examples.Note that in comparative calculation, the differences between prioriprobabilities and posteriori probabilities are calculated. First of all,in step S601, the CPU 100 acquires 11.0%, 48.0%, and 41.0% as the prioriprobabilities of the inference results “abnormality type: primary lungcancer”, “abnormality type: lung metastasis”, and “abnormality type:other abnormalities”, respectively.

FIG. 6 shows a case in which the medical information “size of nodus:intermediate” is selected from I_(fix) input in step S503 by settingk=1, and is set as partial information I₁ of the fixed information. TheCPU 100 performs inference by using I₁ as an evidence. The CPU 100stores the resultant posteriori probabilities, 12.3%, 53.5%, and 34.2%,belonging to the inference results “abnormality type: primary lungcancer”, “abnormality type: lung metastasis”, and “abnormality type:other abnormalities” in association with I₁.

Table 3 shows all I_(j) with k=1, the posteriori probabilities of theinference results A₁ to A_(n) calculated by using I_(j) as evidences,and differences D(A_(i)|I_(j)) between the posteriori probabilities andthe priori probabilities of the inference results A₁ to A_(n). Thesedifferences are obtained by the processing in steps S603 to S605.

TABLE 3 I_(j) (k = 1) D D P (A₁|I_(j)) P (A₂|I_(j)) P (A₃|I_(j)) D(A₁|I_(j)) (A₂|I_(j)) (A₃|I_(j)) Size of 12.3% 53.5% 34.3% 1.3% 5.5%−6.7% Nodus: Intermediate Shape: 4.5% 65.9% 29.6% −6.5% 17.9% −11.4%Sphere Soft Tissue 21.9% 40.4% 37.7% 10.9% −7.6% −3.3% Density Ratio:Low Gas Density 27.5% 37.0% 35.5% 16.5% −11.0% −5.5% Ratio: HighBoundary: 6.0% 59.9% 34.1% −5.0% 11.9% −.6.9% Unclear A₁ AbnormalityType: Primary Lung Cancer P (A₁) = 11.0% A₂ Abnormality Type: LungMetastasis P (A₂) = 48.0% A₃ Abnormality Type: Other Abnormalities P(A₃) = 41.0%

Table 4 shows an example of a method of calculating relationship amountsC(A_(i), I_(j)). In this case, relationship amounts are absolutelyobtained in accordance with the differences between posterioriprobabilities and priori probabilities. Table 5 shows the relationshipamounts obtained by the calculation method indicated by table 4. Theseamounts are obtained by the processing in step S607.

TABLE 4 D(A_(x)|I_(j)) C(A_(x), I_(j)) 14.0%-      +4 10.0%-14.0% +3 6.0%-10.0% +2 2.0%-6.0% +1 −2./0%-2.0%  0 −6.0%-−2.0% −1 −10.0%-−6.0% −2 −14.0%-−10.0% −3     -−14.0% −4

TABLE 5 I_(j)(k = 1) C(A₁, I_(j)) C(A₂, I_(j)) C(A₃, I_(j)) Size ofNodus: 0 +1 −2 Intermediate Shape: Sphere −2 +4 −3 Soft Tissue Density+3 −2 −1 Ratio: Low Gas Density Ratio: High +4 −3 −1 Boundary: Unclear−1 +3 −2 A₁ Abnormality Type: Primary Lung Cancer A₂ Abnormality Type:Lung Metastasis A₃ Abnormality Type: Other Abnormalities

FIG. 12 shows an example of display on the monitor 104 when k=1 and thepredetermined criterion in step S703 is set to the absolute value of arelationship amount which is 3 or more (in other words, relationshipamounts of +4, +3, −3, and −4). In the above case, when i=1 and j=1(size of nodus: intermediate), since the relationship amount is 0, itdoes not satisfy the criterion. The CPU 100 therefore skips theprocessing in step S704, and performs the processing in step S705. Thatis, the monitor 104 displays no data. In contrast, when i=1 (primarylung cancer) and j=4 (gas density ratio: high), since the relationshipamount is +4, the amount satisfies the criterion. The CPU 100 thereforeperforms the processing in step S704.

In the above case, when the relationship amount is positive, itindicates that the posteriori probability of the inference result A_(i)obtained when I_(j) is input as an evidence becomes higher than thepriori probability. When the relationship amount is negative, thecorresponding information indicates that the posteriori probabilitybecomes lower than the priori probability. Therefore, when therelationship amount is positive, the corresponding information becomes areason for affirming the inference result A_(i), whereas when therelationship amount is negative, the corresponding information becomes areason for denying the inference result. When i=1 and j=4, therefore,“gas density ratio: high” is displayed as a reason for affirming theinference result A₁ (primary lung cancer) on the monitor 104.

When all the processing is complete, as shown in FIG. 12, the CPU 100displays “abnormality type: primary lung cancer”, “abnormality type:lung metastasis”, and “abnormality type: other abnormalities” as theinference results A₁ to A_(n) and 15.3%, 66.4%, and 18.3% as theirposteriori probabilities.

The CPU 100 displays “soft tissue density ratio: low” and “gas densityratio: high” as reasons for affirming “abnormality type: primary lungcancer”. In addition, the CPU 100 displays “shape: sphere” and“boundary: unclear” as reasons for affirming “abnormality type: lungmetastasis”, and “gas denying ratio: high” as a reason for denying“abnormality type: lung metastasis”. Furthermore, the CPU 100 displays“shape: sphere” as a reason for denying “abnormality type: otherabnormalities”.

A case in which k=2 will be described next. In the following case, therelationship amount calculation processing in step S607 is performed by“a method of calculating the absolute values of the differences betweencalculated posteriori probabilities and priori probabilities for therespective partial sets, and normalizing the values with reference tothe maximum value among them”. As described in the first embodiment,FIG. 8 shows a case in which the pieces of medical information “size ofnodus: intermediate” and “shape: sphere” are selected from I_(fix) bysetting k=2, and are set as partial information I₁ of the fixedinformation. The CPU 100 performs inference by using I₁ as an evidenceand stores the resultant posteriori probabilities, 4.9%, 71.1%, and24.0%, belonging to the inference results “abnormality type: primarylung cancer”, “abnormality type: lung metastasis”, and “abnormalitytype: other abnormalities” in association with I₁.

Table 6 shows all I_(j) with k=2, the posteriori probabilities of theinference results A₁ to A_(n), and the differences between theposteriori probabilities and the priori probabilities of the inferenceresults A₁ to A_(n). They are obtained by the processing in steps S603to S605.

TABLE 6 I_(j) (k = 2) P (A₁|I_(j)) P (A₂|I_(j)) P (A₃|I_(j)) D(A₁|I_(j)) D (A₂|I_(j)) D (A₃|I_(j)) Size of Shape: 4.9% 71.1% 24.0%−6.1% 23.1% −17.0% Nodus: Sphere Intermediate Size of Soft Tissue 24.2%44.6% 31.2% 13.2% −3.4% −9.8% Nodus: Density Intermediate Ratio: LowSize of Gas Density 30.2% 40.6% 29.2% 19.2% −7.4% −11.8% Nodus: Ratio:High Intermediate Size of Boundary: 6.6% 65.4% 28.0% −4.4% 17.4% −13.0%Nodus: Unclear Intermediate Shape: Soft Tissue 9.8% 60.5% 29.7% −1.2%12.5% −11.3% Sphere Density Ratio: Low Shape: Gas Density 12.9% 57.9%29.3% 1.9% 9.9% −11.7% Sphere Ratio: High Shape: Boundary: 2.3% 75.2%22.5% −8.7% 27.2% −18.5% Sphere Unclear Soft tissue Gas Density 46.2%26.3% 27.5% 35.2% −21.7% −13.5% Density Ratio: High Ratio: Low Softtissue Boundary: 12.8% 53.8% 33.4% 1.8% 5.8% −7.6% Density UnclearRatio: Low Gas Density Boundary: 16.7% 50.8% 32.5% 5.7% 2.8% −8.5%Ratio: High Unclear A₁ Abnormality Type: Primary Lung Cancer P (A₁) =11.0% A₂ Abnormality Type: Lung Metastasis P (A₂) = 48.0% A₃ AbnormalityType: Other Abnormalities P (A₃) = 41.0%

The CPU 100 obtains relationship amounts by the processing in step S607.In this case, the CPU 100 calculates the absolute values ofD(A_(i)|I_(j)) (to be referred to as difference amounts hereinafter) andnormalizes them based on the maximum value among them. These amountswill be described specifically below. The maximum value of the absolutevalues of the difference amounts of “abnormality type: primary lungcancer” is 35.2%. The respective difference amounts are converted toconvert this value into 4.0. If, for example, the difference amount is−4.4%, the relationship amount becomes −0.5. Thereafter, this value isrounded down to the nearest 1, and the obtained value is used as arelationship amount. That is, 4.0 is rounded down to +4, and −0.5 isrounded down to 0. This operation is also performed for “abnormalitytype: lung metastasis” and “abnormality type: other abnormalities”. Atthis time, the value on which normalization is based changes inaccordance with inference results. In this case, normalization isperformed based on 27.2% in the case of “abnormality type: lungmetastasis” and 18.5% in the case of “abnormality type: otherabnormalities”. Table 7 shows the relationship amounts obtained by theabove method.

TABLE 7 I_(j)(k = 2) C(A₁|I_(j)) C(A₂|I_(j)) C(A₃|I_(j)) Size of Nodus:Shape: Sphere 0 +3 −3 Intermediate Size of Nodus: Soft Tissue +1 0 −2Intermediate Density Ratio: Low Size of Nodus: Gas Density +2 −1 −2Intermediate Ratio: High Size of Nodus: Boundary: 0 +2 −2 IntermediateUnclear Shape: Sphere Soft Tissue 0 +1 −2 Density Ratio: Low Shape:Sphere Gas Density 0 +1 −2 Ratio: High Shape: Sphere Boundary: 0 +4 −4Unclear Soft tissue Gas Density +4 −3 −2 Density Ratio: Ratio: High LowSoft tissue Boundary: 0 0 −1 Density Ratio: Unclear Low Gas DensityBoundary: 0 0 −1 Ratio: High Unclear A₁ Abnormality Type: Primary LungCancer A₂ Abnormality Type: Lung Metastasis A₃ Abnormality Type: OtherAbnormalities

FIG. 13 shows an example of display on the monitor 104 when k=2 and thepredetermined criterion in step S703 is set to the absolute value of arelationship amount which is 3 or more (in other words, relationshipamounts of +4, +3, −3, and −4). As in the case of k=1, when therelationship amount is positive, the corresponding information becomes areason for affirming the inference result A_(i), whereas when therelationship amount is negative, the corresponding information becomes areason for denying the inference result. As shown in FIG. 13, when allthe processing is complete, the CPU 100 displays “abnormality type:primary lung cancer”, “abnormality type: lung metastasis”, and“abnormality type: other abnormalities” as the inference results A₁ toA_(n) and 15.3%, 66.4%, and 18.3% as their posteriori probabilities.

The CPU 100 displays “soft tissue density ratio: low and gas densityratio: high” as reasons for affirming “abnormality type: primary lungcancer”. In addition, the CPU 100 displays “size of nodus: intermediateand shape: sphere” and “shape: sphere, boundary: unclear” as reasons foraffirming “abnormality type: lung metastasis”, and “soft tissue densityratio: low and gas denying ratio: high” as reasons for denying“abnormality type: lung metastasis”. Furthermore, the CPU 100 displays“size of nodus: intermediate and shape: sphere” and “shape: sphere andboundary: unclear” as reasons for denying “abnormality type: otherabnormalities”.

In either of the cases of k=1 and k=2, it is preferable to display theinference results A₁ to A_(n) in descending order of posterioriprobability calculated by using I_(fix) as evidences, as shown in FIGS.12 and 13. However, the present invention is not limited to this.

Displaying both reasons for affirming an inference result and reasonsfor denying the inference result can make the user consider thepossibility of other inference results as well as determining thereliability of an inference result with the highest probability. Inaddition, as in the first embodiment, this can also prompt the user toverify the reliability of input medical information.

According to the arrangement described above, the following effects canbe obtained:

(1) Presenting a reason for affirming each of a plurality of inferenceresults and a reason for denying it makes it possible to not onlydetermine the reliability of an inference result with the highestprobability but also consider the possibility of other inferenceresults.(2) The arrangement can prompt the user to verify the reliability ofinput medical information.

Modification of Second Embodiment

When selecting partial information I_(j) of fixed information in stepS603, it is possible to select k pieces of information or less (forexample, partial information with both k=1 and k=2) of I_(fix). As acomparative calculation method in step S605, the method of calculatingthe difference values between probabilities has been exemplified.However, the present invention is not limited to this. For example, amethod of calculating probability ratios may be used as a comparativecalculation method in step S605. Other methods may also be used. Inaddition, it is possible to use a relationship amount calculation methodin step S607 other than that described above. For example, relationshipamounts may be calculated by a method of calculating logarithms. When amethod like that described in the case of k=1 is to be used, theconversion width to be set is not limited to that shown in table 4. Inthe case of k=2, it is possible to use the round-up method, theround-off method, or other methods in place of the round-down method.Although the above case uses nine discrete values, the number ofdiscrete values to be used is not limited to this. Furthermore, arelationship amount may take a continuous value.

The determination criterion to be used in step S703 is not limited tothe method described above. The user may arbitrarily change thedetermination criterion. In this case, this apparatus preferablyincludes a user interface for the change of the determination criterion.In step S704, all the pieces of information I_(j) satisfying thecriterion are displayed. However, of all the pieces of informationsatisfying the criterion, only results which affirm/deny the inferenceresult most may be displayed. Furthermore, if there is no reason fordenying an inference result, a warning may be displayed. A determinationcriterion for this display of a warning may differ from that in stepS703. It is, however, not desirable to perform determination based on acriterion milder than the determination criterion in step S703. When arelationship amount and I_(j) are input as evidences, it is alsopossible to simultaneously display posteriori probabilities.

In step S707, all the inference results A₁ to A_(n) are displayed.However, only an inference result with the highest posterioriprobability may be displayed. Alternatively, only some inference resultsmay be displayed. In this case, the user may determine the number ofinference results to be displayed. In addition, inference results withposteriori probabilities exceeding a threshold, for example, posterioriprobabilities equal to or more than 30%, may be displayed. However, thethreshold to be set is not limited to the above example, and the usermay determine a threshold.

Obviously, the modification described in the first embodiment inassociation with steps S201 and S204 can be applied to the secondembodiment.

Third Embodiment

The third embodiment will be described next. Note that the arrangementof the third embodiment is the same as that of the first embodiment. Forthis reason, the block diagram of FIG. 1 will be used, and a descriptionof the arrangement will be omitted. Control performed by a control unit10 in the third embodiment is almost the same as that in the firstembodiment (FIG. 2). FIG. 14 is a flowchart for explaining a procedurefor inference result display processing (S205) in the third embodiment.Note that the processing shown in the flowchart of FIG. 14 isimplemented by causing a CPU 100 to execute programs stored in a mainmemory 101. In step S205, the CPU 100 displays the results of inferenceprocessing performed in step S204. A detailed processing procedure instep S205 will be described in detail below with reference to FIGS. 14and 15.

FIG. 14 is a flowchart showing a detailed processing procedure in stepS205. In step S901, the CPU 100 calculates an evaluation value V(A_(x))of A_(x) with the highest posteriori probability among the posterioriprobabilities of A₁ to A_(n) stored in the main memory 101. Note thatthis calculation method will be described later.

In step S902, the CPU 100 determines whether the evaluation valuecalculated in step S901 satisfies a predetermined criterion. If NO instep S902, the CPU 100 performs the processing in step S903. If YES instep S902, the CPU 100 skips the processing in step S903. In step S903,the CPU 100 displays a warning on the monitor 104. In step S904, the CPU100 displays the result of inference processing performed in step S804.The CPU 100 displays the inference results A₁ to A_(n), their posterioriprobabilities, and I_(H) and I_(L) stored in the main memory 101 on themonitor 104. A concrete example of this operation will be describedbelow.

Table 8 shows I_(fix) input to the probability reasoning model in FIG.4B in step S803, the posteriori probabilities of the inference resultsA₁ to A_(n) (n=3), calculated by using I_(fix) as evidences, and I_(H)and I_(L) when k=1.

TABLE 8 I_(fix) Size of Nodus: Intermediate Smoothness: IntermediateVascular Involvement/Involution: Unknown Soft Tissue Density Ratio: HighGas Density Ratio: Low A₁ Abnormality Type:  9.6% Primary Lung Cancer A₂Abnormality Type: 49.9% Lung Metastasis A₃ Abnormality Type: 40.5% OtherAbnormalities I_(H) Vascular Involvement/Involution: Unknown I_(L) GasDensity Ratio: Low

In step S901, the CPU 100 calculates the evaluation value of a diagnosisresult (abnormality type) having the highest posteriori probabilityamong A₁ to A_(n). In this case, the CPU 100 calculates an evaluationvalue V(A₂) of A₂.

As a method of calculating the evaluation value V(A_(x)) of A_(x), thereis available a method of calculating the difference between theprobability of an inference result (A_(x)) with the highest posterioriprobability and the probability of an inference result (A_(x2)) with thesecond highest posteriori probability. There is also available a methodof calculating an evaluation value by using the probability of aninference result with the highest posteriori probability and the numberof states (=n) of the inference result. These methods are expressed byequations (2) and (3), respectively.

$\begin{matrix}{\left\lbrack {{Mathematical}\mspace{14mu} 2} \right\rbrack \mspace{571mu}} & \; \\{{V\left( A_{x} \right)} = {{P\left( {A_{x}I_{fix}} \right)} - {P\left( {A_{x\; 2}I_{fix}} \right)}}} & (2) \\{\left\lbrack {{Mathematical}\mspace{14mu} 3} \right\rbrack \mspace{571mu}} & \; \\{{V\left( A_{x} \right)} = {{P\left( {A_{x}I_{fix}} \right)} - \frac{1}{n}}} & (3)\end{matrix}$

In this case, P(A_(x)|I_(fix)) indicates the posteriori probability ofthe inference result A_(x) calculated by using I_(fix) as evidences. Inthe case of table 8, the inference result A₂ exhibits the highestposteriori probability, and the inference result A₃ exhibits the secondhighest posteriori probability, which are 49.9% and 40.5%, respectively.In addition, n is 3, and its reciprocal is 1/n=0.333, which is 33.3% ona percentage basis. Calculating V(A₂) by using equation (2) will produceV(A₂)=49.9%−40.5%=9.4%. Calculating V(A₂) by using equation (3) willproduce V(A₂)=49.9%−33.3%=16.6%.

In step S902, the CPU 100 determines whether V(A₂) satisfies apredetermined criterion. In this case, the CPU 100 uses a threshold asthe predetermined criterion, and performs determination depending onV(A₂) is equal to or less than the threshold. If V(A₂) is equal to orless than the threshold, the CPU 100 performs the processing in stepS903. If V(A₂) exceeds the threshold, the CPU 100 performs theprocessing in step S904. It is preferable that the user can change thisthreshold. In the case of table 8, when, for example, the threshold is15%, the CPU 100 performs the processing in step S903 with respect tothe evaluation value calculated by equation (2). In contrast, with theevaluation value calculated by equation (3), the CPU 100 skips theprocessing in step S903. In step S903, the CPU 100 displays a warning.In step S904, the CPU 100 displays I_(fix), the posteriori probabilitiesof the inference results A₁ to A₃, and I_(H) and I_(L). FIG. 15 shows anexample of display when a warning is displayed. In this case, a warningis displayed by using an icon calling attention and characters.

According to the above arrangement, the following effect can beobtained.

(1) Displaying a warning when an evaluation value calculated for aninference result satisfies a predetermined criterion can prompt the userto verify the reliability of the inference result and the reliability ofinput medical information.

Modification of Third Embodiment

It is also possible to calculate an evaluation value in step S901 byusing a combination of equations (2) and (3). Equations other thanequations (2) and (3) may also be used. The user may arbitrarily set acriterion in step S902. In this case, this apparatus preferably includesa user interface for setting conditions. However, the present inventionis not limited to this. The criterion to be used is not limited to athreshold.

When displaying a warning in step S903, it is possible to display onlyan icon or characters. Alternatively, it is possible to use othermethods as long as they take forms of calling attention to the user. Forexample, there is available a method of changing the display colors ofcharacters, producing a warning sound, or changing a background color.In step S904, all the inference results A₁ to A_(n) are displayed. Itis, however, possible to display only an inference result with thehighest posteriori probability. Alternatively, only some inferenceresults may be displayed. In this case, the user may determine thenumber of inference results to be displayed. In addition, inferenceresults with posteriori probabilities exceeding a threshold, forexample, posteriori probabilities equal to or more than 30%, may bedisplayed. However, the threshold to be set is not limited to the aboveexample, and the user may determine a threshold.

As has been described in detail above, according to the first to thirdembodiments, of a plurality of reasons for inference, reasons havinggreat influences on an inference result are presented to the user.Presenting also reasons for denying the inference result can make theuser feel the necessity to consider the reliability of medicalinformation and inference results and diagnosis other than a presentedinference result (an inference result exhibiting the highestprobability). That is, there is provided a mechanism of examining thereliability of information input at the time of interpretation and thepossibility of diagnosis other than that presented by the system.

Other Embodiments

Although embodiments have been described in detail above, the presentinvention can take embodiments as a system, apparatus, method, program,storage medium, and the like. More specifically, the present inventioncan be applied to a system including a plurality of devices, or to anapparatus including a single device.

The present invention is also implemented by executing the followingprocessing. That is, the processing is executed by supplying software(programs) for implementing the functions of the above embodiments tothe system or apparatus via a network or various types of storage media,and causing the computer (or the CPU or MPU) of the system or apparatusto read out and execute the programs.

The present invention is not limited to the above embodiments andvarious changes and modifications can be made within the spirit andscope of the present invention. Therefore, to apprise the public of thescope of the present invention, the following claims are made.

This application claims the benefit of Japanese Patent Application No.2009-047021, filed Feb. 27, 2009 which is hereby incorporated byreference herein in its entirety.

1. A medical decision making support apparatus comprising: an inferenceunit configured to obtain an inference result by performing inferenceprocessing associated with a medical diagnosis based on a plurality ofpieces of input medical information; a calculation unit configured tocalculate a degree of one of denial and affirmation of the inferenceresult in association with each of a plurality of partial sets includingeach medical information extracted from the plurality of pieces ofmedical information as an element; and a presentation unit configured topresent an inference result obtained by said inference unit and negativeinformation indicating medical information included in a partial set, ofthe plurality of partial sets, for which a degree of denial iscalculated by said calculation unit.
 2. The medical decision makingsupport apparatus according to claim 1, wherein said presentation unitpresents, as affirmative information, medical information included in apartial set, of the plurality of partial sets, for which a degree ofaffirmation is calculated by said calculation unit.
 3. The medicaldecision making support apparatus according to claim 2, wherein saidpresentation unit presents medical information included in a partial setfor which a highest degree of denial is calculated by said calculationunit and medical information included in a partial set for which ahighest degree of affirmation is calculated, as the negative informationand the affirmative information, respectively.
 4. The medical decisionmaking support apparatus according to claim 1, wherein said inferenceunit obtains an inference result by calculating a posteriori probabilityof each inference result based on the plurality of pieces of medicalinformation in association with a plurality of inference results forwhich priori probabilities are set.
 5. The medical decision makingsupport apparatus according to claim 4, wherein said calculation unitcalculates the degree for an inference result exhibiting the highestposteriori probability.
 6. The medical decision making support apparatusaccording to claim 4, wherein said calculation unit calculates thedegree for each of the plurality of inference results, and saidpresentation unit presents medical information of a partial set forwhich the degree satisfies a predetermined condition, as one ofaffirmation information and denial information, for each of theplurality of inference results.
 7. The medical decision making supportapparatus according to claim 4, wherein said calculation unit calculatesthe degree for each inference result, of the plurality of inferenceresults, whose posteriori probability obtained by the inferenceprocessing exceeds a predetermined value.
 8. The medical decision makingsupport apparatus according to claim 4, wherein said calculation unitcalculates a posteriori probability of each inference result based onthe partial set, and calculates the degree by using the prioriprobability and the posteriori probability.
 9. The medical decisionmaking support apparatus according to claim 4, further comprising anevaluation unit configured to calculate an evaluation value for ahighest posteriori probability of a plurality of posterioriprobabilities calculated in association with the plurality of inferenceresults, based on the plurality of posteriori probabilities, whereinsaid presentation unit presents a warning associated with reliability ofan inference result when the evaluation value does not satisfy apredetermined condition.
 10. The medical decision making supportapparatus according to claim 9, wherein said evaluation unit sets, asthe evaluation value, a difference between a highest posterioriprobability, of the plurality of probabilities, and a second highestposteriori probability.
 11. The medical decision making supportapparatus according to claim 9, wherein said evaluation unit sets, asthe evaluation value, a difference between a highest posterioriprobability of the plurality of posteriori probabilities and areciprocal of the number of the plurality of posteriori probabilities.12. A control method for a medical decision making support apparatus,comprising: an inference step of obtaining an inference result byperforming inference processing associated with medical diagnosis basedon a plurality of pieces of input medical information; a calculationstep of calculating a degree of one of denial and affirmation of theinference result in association with each of a plurality of partial setsincluding each medical information extracted from the plurality ofpieces of medical information as an element; and a presentation step ofpresenting an inference result obtained in the inference step andnegative information indicating medical information included in apartial set, of the plurality of partial sets, for which a degree ofdenial is calculated in the calculation step.
 13. A program for causinga computer to execute each step of a control method defined in claim 12.